In this study, dynamic Bayesian networks have been applied to predict future biomass of geographically different but functionally equivalent fish species. A latent variable is incorporated to model functional collapse, where the underlying food web structure dramatically changes irrevocably (known as a regime shift). We examined if the use of a hidden variable can reflect changes in the trophic dynamics of the system and also whether the inclusion of recognised statistical metrics would improve predictive accuracy of the dynamic models. The hidden variable appears to reflect some of the metrics’ characteristics in terms of identifying regime shifts that are known to have occurred. It also appears to capture changes in the variance of different species biomass. Including metrics in the models had an impact on predictive accuracy but only in some cases. Finally, we explore whether exploiting expert knowledge in the form of diet matrices based upon stomach surveys is a better approach to learning model structure than using biomass data alone when predicting food web dynamics. A non-parametric bootstrap in combination with a greedy search algorithm was applied to estimate the confidence of features of networks learned from the data, allowing us to identify pairwise relations of high confidence between species.